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Erschienen in:
Use of Bayesian Networks for System Reliability Assessment Kapitel lesen Erstes Kapitel lesen

2019 | OriginalPaper | Buchkapitel

A Hybrid Intuitionistic Fuzzy and Entropy Weight Based Multi-Criteria Decision Model with TOPSIS

verfasst von : Nitin Sachdeva, P. K. Kapur

Erschienen in: System Performance and Management Analytics

Verlag: Springer Singapore

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Abstract

In a scenario where decision-makers are always faced with the challenge of selecting the right technology for their IT needs posed due to the availability of multiple advanced technologies in the market and consequences related to wrong selection, Intuitionistic Fuzzy Sets (IFSs) have demonstrated effectiveness in dealing with such vagueness and hesitancy in the decision-making process. Here in this paper, we propose a hybrid IFS and entropy weight based Multi-Criteria Decision Model (MCDM) with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method. The model helps measure the exactness and vagueness of each alternative over several criteria. An Intuitionistic Fuzzy Weighted Approach (IFWA) operator for aggregating individual decision-maker’s opinions regarding each alternative over every criterion is employed. Additionally, Shannon’s entropy method is used to measure criteria weights separately. We apply the proposed model in selection of cloud solution for managing big data projects.

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Vorheriges Kapitel Two-Dimensional Vulnerability Patching Model
Nächstes Kapitel The Role of Website Personality and Website User Engagement on Individual’s Purchase Intention
Literatur
1.
Atanassov, K., Pasi, G., & Yager, R. R. (2005). Intuitionistic fuzzy interpretations of multi-criteria multi-person and multi-measurement tool decision making. International Journal of Systems Science, 36(14), 859–868.CrossRef
2.
Atanassov, K. T. (1999). Intuitionistic fuzzy sets. Heidelberg: Springer.CrossRef
3.
Atanassov, K. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20, 87–96.CrossRef
4.
Bustince, H., & Burillo, P. (1996). Vague sets are intuitionistic fuzzy sets. Fuzzy Sets and Systems, 79, 403–405.CrossRef
5.
Gau, W. L., & Buehrer, D. J. (1993). Vague sets. IEEE Transactions on Systems Man and Cybernetics, 23(2), 610–614.CrossRef
6.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of big data on cloud computing: Review and open research issues. Information Systems, 47, 98–115.CrossRef
7.
Hwang, C. L., & Yoon, K. (1981). Multiple attribute decision making-methods and applications: A state-of-the-art survey. New York: Springer.CrossRef
8.
Liu, H. W., & Wang, G. J. (2007). Multi-criteria decision-making methods based on intuitionistic fuzzy sets. European Journal of Operational Research, 179, 220–233.CrossRef
9.
De Luca, A., & Termini, S. (1972). A definition of a non-probabilistic entropy in the setting of fuzzy sets theory. Information and Control, 20, 301–312.CrossRef
10.
Shannon, C. E. (1948). The mathematical theory of communication. Bell System Technical Journal, 27(379–423), 623–656.CrossRef
11.
Shu, M. S., Cheng, C. H., & Chang, J. R. (2006). Using intuitionistic fuzzy sets for fault tree analysis on printed circuit board assembly. Microelectronics Reliability, 46(12), 2139–2148.CrossRef
12.
Szmidt, E., & Kacprzyk, J. (2001). Intuitionistic fuzzy sets in some medical applications. Lecture Notes in Computer Science, 2206, 148–151.CrossRef
13.
Szmidt, E., & Kacprzyk, J. (2004). A similarity measure for intuitionistic fuzzy sets and its application in supporting medical diagnostic reasoning. Lecture Notes in Computer Science, 3070, 388–393.CrossRef
14.
Szmidt, E., & Kacprzyk, J. (2000). Distances between intuitionistic fuzzy sets. Fuzzy Sets and Systems, 114, 505–518.CrossRef
15.
Szmidt, E., & Kacprzyk, J. (2001). Entropy of intuitionistic fuzzy sets. Fuzzy Sets and Systems, 118, 467–477.CrossRef
16.
Vlachos, I. K., & Sergiadis, G. D. (2007). Intuitionistic fuzzy information—Applications to pattern recognition. Pattern Recognition Letters, 28, 197–206.CrossRef
17.
Wang, P. (2009). QoS-aware web services selection with intuitionistic fuzzy set under consumer’s vague perception. Expert Systems with Applications, 36(3), 4460–4466.CrossRef
18.
Wang, W. Q., & Xin, X. L. (2005). Distance measure between intuitionistic fuzzy sets. Pattern Recognition Letters, 26, 2063–2069.CrossRef
19.
Xu, Z. S. (2007). Intuitionistic fuzzy aggregation operators. IEEE Transactions on Fuzzy Systems, 15(6), 1179–1187.CrossRef
20.
Yeh, C.-H. (2002). A problem-based selection of multi-attribute decision-making methods. International Transactions in Operational Research, 9, 169–181.CrossRef
21.
Zadeh, L. A. (1965). Fuzzy set. Information and Control, 8(3), 338–356.CrossRef
22.
Zeleny, M. (1982). Multiple criteria decision making. New York: McGraw-Hill.
23.
Zhang, C. Y., & Fu, H. Y. (2006). Similarity measures on three kinds of fuzzy sets. Pattern Recognition Letters, 27, 1307–1317.CrossRef
Metadaten
Titel
A Hybrid Intuitionistic Fuzzy and Entropy Weight Based Multi-Criteria Decision Model with TOPSIS
verfasst von
Nitin Sachdeva
P. K. Kapur
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
System Performance and Management Analytics

Print ISBN: 978-981-10-7322-9

Electronic ISBN: 978-981-10-7323-6

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-10-7323-6_27